Spplot(ELEV.CO, main = 'Quantile of DEM (m)', Polys<- list('sp.lines', as(CO.STATE, 'SpatialLines'), col='grey', lty=1) # CO state boundary rgb.palette <- colorRampPalette(c('lightblue','blue','green','yellow','red'), Before that we will create a custom color palette using colorRampPalette of RColorBrewer. We will use sppot function from sp package to plot raster data with state boundary shape file. Now, we plot the DEM raster using the this custom break. Then, you have to create a object with a quantile interval applying classIntervals function to vector data. For details, please see help, just type ?classIntervals in R consoleįirst, we have to convert raster to vector using values function.

The classIntervals function is create a custom breaks of raster values using different styles (such as “fixed”, “sd”, “equal”, “pretty”, “quantile”, “kmeans”, “hclust”, “bclust”, “fisher”, & “jenks”). If you want create a raster map using the quantile, you fave to use classInt package. Now we plot the reclassified raster using plot function with a nice looking legend plot(ELEV.CO.reclass,Ĭol = c('green', 'blue', 'red'), axes = FALSE, You can view the distribution of pixels assigned to each class using the bar-plot(). Now you have to apply reclassify() function of raster package using re-class_m object. Next, you have to reshape the data frame to matrix using matrix function reclass_m <- matrix(reclass_df, First, you have to create classification matrix We will use breaks of histogram to reclassify the DEM raster. Now, you can check where are breaks and how many pixels in each category, and use these breaks to plot a raster layer HISTO.CO$breaks # 1000 2000 3000 4000 HISTO.CO$counts # 5347 4083 1335 # plot using the histogram breaks. Main='Distribution of Elevation values with 4 breaks', # tittle of the plot Landsat 8 Image Processing & Visualizationīefore reclassification, we create a histogram with 4 breaks, of ELEV.CO raster HISTO.COConditional Simulation for Spatial Uncertainty.Assessing the Quality of Spatial Predictions.Geostatistical Methods for Spatial Interpolation.

Deterministic Methods for Spatial Interpolation.

Geographically Weighted Poisson Regression.

Geographically Weighted Principal Components Analysis.

Geographically Weighted Summary Statistics.

Map Projection and Coordinate Reference Systems.

Plot raster in r hillside